Date of Award
Summer 2017
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mechanical Engineering
First Advisor
Casey, Allen M.
Second Advisor
Nagurka, Mark
Third Advisor
Roy, Somesh
Abstract
There are numerous motivations for improvements in automotive fuel efficiency. As concerns over the environment grow at a rate unmatched by hybrid and electric automotive technologies, the need for reductions in fuel consumed by current road vehicles has never been more present. Studies have shown that a major cause of poor fuel consumption in automobiles is improper driving behavior, which cannot be mitigated by purely technological means. The emergence of autonomous driving technologies has provided an opportunity to alleviate this inefficiency by removing the necessity of a driver. Before autonomous technology can be relied upon to reduce gasoline consumption on a large scale, robust programming strategies must be designed and tested. The goal of this thesis work was to design and deploy an autonomous control algorithm to navigate a four cylinder, gasoline combustion engine through a series of changing load profiles in a manner that prioritizes fuel efficiency. The experimental setup is analogous to a passenger vehicle driving over hilly terrain at highway speeds. The proposed approach accomplishes this using a model-predictive, real-time optimization algorithm that was calibrated to the engine. Performance of the optimal control algorithm was tested on the engine against contemporary cruise control. Results indicate that the “efficient” strategy achieved one to two percent reductions in total fuel consumed for all load profiles tested. The consumption data gathered also suggests that further improvements could be realized on a different subject engine and using extended models and a slightly modified optimal control approach.